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High Quality Content by WIKIPEDIA articles! Tikhonov regularization is the most commonly used method of regularization of ill-posed problems named for Andrey Tychonoff. In statistics, the method is also known as ridge regression. It is related to the Levenberg-Marquardt algorithm for non-linear least-squares problems. In several fields of mathematics, in particular statistics, machine learning and inverse problems, regularization involves introducing additional information in order to solve an ill-posed problem or prevent overfitting. This information is usually of the form of a penalty for complexity, such as restrictions for smoothness or bounds on the vector space norm.